Gamos has contributed to a ground-breaking study aimed at investigating the ‘poverty reach’ of Compassion's child sponsorship programme working in 26 countries. This study seeks to answer the question: How can local partners ensure that it is the poorest households and individuals that benefit from their programmes?

There are several ways in which poverty can be measured. Money-metric measures such as the United Nations poverty threshold figure of $2 per day are commonly used. Access to basic needs such as water, shelter and food can be used as a measure of poverty. More recently, building upon the livelihoods approach, indicators covering health, education and household assets have also been used to gauge poverty levels, as in the Multidimensional Poverty Index (MPI) adopted by UNDP in the Human Development Report.

Money metric measures are the easiest to understand and compare, but can be highly problematic. Gathering data at the micro level is a technical and complicated process with social factors often leading to inaccurate disclosures.

Compassion has as it’s stated aim, to work with the poorest of the poor. However, by what means can their local partners accurately assess which households qualify for assistance?

In order to answer this, data was gathered from samples in 8 countries where Compassion works. Two approaches were then applied:

A comprehensive range of indicators of monetary and agricultural flows associated with each household were manipulated to arrive at per capita income figures, enabling the proportion below the PPP $2 poverty line to be calculated.

A combined assessment based on a number of commonly applied poverty indicators, both monetary and non-monetary was carried out. Indices were created for: education and literacy; under five morbidity; access to public services; and ownership of key assets.

What alternative means could be used to ensure the poorest are reached?

It is possible to draw up a list of ‘obvious’ aspects of poverty that are relevant to the local context, which local partners can use as a prompt when selecting 'qualifying' families. Note that these should not become a ridged list of criteria that need to be ticked off. These need to be obvious, observable and non-intrusive. They can include, for example:

Access to employment

Ability to feed the family

Quality of housing

Household assets, e.g. television, mobile phone, etc

Number of people sharing a room

Appropriate access to water and sanitation facilities

General state of the home

Obvious signs of malnutrition and poor physical development

Tests were carried out to determine which variables correlated with income measures and could, therefore, be considered as alternative measures. The findings revealed that, in almost all countries studied, ownership of a mobile phone and a composite household assets index correlated with per capita income. Furthermore, in 6 out of the 8 countries, the subjective observation of the enumerator regarding the precarious nature of the household situation also correlated with per capita income.

Other indicators that were commonly linked to income measures included dependency ratios (5 out of the 8 countries), problems satisfying food needs, status compared to others in the community, and level of education (4 out of 8 countries).

One of the most interesting aspects of several of these indicators is that they reflect the subjective observations of either the enumerator or the respondent. This suggests that trained enumerators can indeed assess poverty levels to a reasonable level of accuracy without access to complex and problematic data gathering tools and sources.

The study notes that there were significant differences between the countries regarding which proxy indicators correlated most closely with household per capita income. Very few were common to the majority of countries assessed. Therefore locally relevant indicators need to be identified.